Accessing Artificial Intelligence for Fetus Health Status Using Hybrid Deep Learning Algorithm (AlexNet-SVM) on Cardiotocographic Data.

Muhammad Hussain, Nadia; Rehman, Ateeq Ur; Othman, Mohamed Tahar Ben; Zafar, Junaid; Zafar, Haroon; Hamam, Habib

Muhammad Hussain, Nadia; Rehman, Ateeq Ur; Othman, Mohamed Tahar Ben; Zafar, Junaid; Zafar, Haroon; Hamam, Habib.

Afiliação

Muhammad Hussain N; Lambe Institute for Translational Research, National University of Ireland Galway, H91TK33 Galway, Ireland.
Rehman AU; College of Engineering and Informatics, National University of Ireland Galway, H91TK33 Galway, Ireland.
Othman MTB; Department of Electrical Engineering, Government College University, Lahore 54000, Pakistan.
Zafar J; Department of Electrical Engineering, Government College University, Lahore 54000, Pakistan.
Zafar H; Department of Computer Science, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia.
Hamam H; Department of Electrical Engineering, Government College University, Lahore 54000, Pakistan.

Sensors (Basel) ; 22(14)2022 Jul 07.

Article em En | MEDLINE | ID: mdl-35890783

Assuntos

Inteligência Artificial; Aprendizado Profundo; Algoritmos; Feto; Nível de Saúde; Humanos; Redes Neurais de Computação; Máquina de Vetores de Suporte

Palavras-chave

artificial intelligence; cardiotocography; clinical settings; deep neural networks; fetus classification; transfer learning

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Aprendizado Profundo Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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